Abstract
Present Brain-Computer Interfacing (BCI) technology allows inference and detection of cognitive and affective states, but fairly little has been done to study scenarios in which such information can facilitate new applications that rely on modeling human cognition. One state that can be quantified from various physiological signals is attention. Estimates of human attention can be used to reveal preferences and novel dimensions of user experience. Previous approaches have tackled these incredibly challenging tasks using a variety of behavioral signals, from dwell-time to click-through data, and computational models of visual correspondence to these behavioral signals. However, behavioral signals are only rough estimations of the real underlying attention and affective preferences of the users. Indeed, users may attend to some content simply because it is salient, but not because it is really interesting, or simply because it is outrageous. With this paper, we put forward a research agenda and example work using BCI to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. Subsequently, we link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
Abstract (translated)
当前的脑机接口（BCI）技术允许推断和检测认知和情感状态，但相当少的工作已经致力于研究这种信息如何促进新的应用，这些应用依赖于对人类认知的建模。一个可以从各种生理信号进行测量的状态是注意力。人类注意力的估计可用于揭示用户的偏好和新的用户体验维度。之前的方法已经利用各种行为信号（从驻留时间到点击通过数据）和视觉对应关系的计算模型来解决这些极其困难的问题。然而，行为信号只是对用户真实关注和情感偏好的粗略估计。事实上，用户可能只关注某些内容，因为它们引人注目，而不是因为它们真正有趣，或者只是因为它们很离奇。在本文中，我们提出了使用BCI推断用户偏好、他们对视觉内容的注意相关性和情感经验之间的关联的研究议程和示例工作。随后，我们将这些与相关应用联系起来，如信息检索、个性化的生成模型驱动和民办公众情绪体验的估算。
URL
https://arxiv.org/abs/2405.09691
